ENGSCI 763 : Advanced Simulation and Stochastic Optimisation


2021 Semester Two (1215) (15 POINTS)

Course Prescription

Advanced simulation topics with an emphasis on optimisation under uncertainty. Uniform and non-uniform random variate generation, input distribution selection, output analysis, variance reduction. Simulation-based optimisation and stochastic programming. Two-stage and multi-stage programs with recourse. Modelling risk. Decomposition algorithms. Scenario construction and solution validation.

Course Overview

Every decision problem involves some uncertainty. This course aims to help students understand how to incorporate this feature in optimization models. The focus will be on probabilistic models of uncertainty (called stochastic optimization) but we also include some robust optimization techniques (when probability distributions are assumed to be unavailable, even as estimates).
Example applications are in optimal capacity planning (for future uncertain demand) in energy, telecommunications and production systems, revenue optimization in airline, hotel and rental car industries, farm planning in agriculture with uncertain weather, and so on.
The first half of the course will focus on modelling uncertainty using probability distributions for use in simulations. The second half of the course will focus on specific optimization models that incorporate these probability distributions.

Course Requirements

Prerequisite: ENGSCI 391 or 765

Capabilities Developed in this Course

Capability 1: Disciplinary Knowledge and Practice
Capability 2: Critical Thinking
Capability 3: Solution Seeking

Learning Outcomes

By the end of this course, students will be able to:
  1. Demonstrate an understanding of probability spaces, random variables, and applying probability theory in modelling. (Capability 1 and 2)
  2. Demonstrate an understanding of optimization under uncertainty and how results differ from deterministic optimization. (Capability 1 and 2)
  3. Be able to analyse results from Monte Carlo simulations (Capability 3)
  4. Be able to formulate two-stage stochastic programs with recourse (Capability 2 and 3)
  5. Be able to solve examples using the L-shaped method (Capability 3)
  6. Be able to formulate models using conditional value at risk (Capability 2 and 3)
  7. Demonstrate an understanding of modelling with chance constraints (Capability 1, 2 and 3)
  8. Demonstrate an understanding of coherent risk measures (Capability 1, 2 and 3)
  9. Develop an understanding of robust optimization techniques and their application. (Capability 1, 2 and 3)


Assessment Type Percentage Classification
Assignments 40% Individual Coursework
Final Exam 60% Individual Coursework
Assessment Type Learning Outcome Addressed
1 2 3 4 5 6 7 8 9
Final Exam

Workload Expectations

This course is a standard 15 point course and students are expected to spend 10 hours per week involved in each 15 point course that they are enrolled in.

For this course, you can expect 3 hours of lectures,  3 hours of reading and thinking about the content and 4 hours of work on assignments and/or test preparation.

Some lectures will be given as tutorials. Extra lectures might be scheduled to make up for lost time from systems week.

Delivery Mode

Campus Experience

Attendance is required at scheduled activities to complete components of the course,  unless precluded by University closure due to lockdown.
Lectures will be available as recordings.
The course will not include live online events.
Attendance on campus is required for the exam, unless precluded by University closure due to lockdown.
The activities for the course are scheduled as a standard weekly timetable.

Health & Safety

Students are expected to comply with standard health and safety requirements laid out by School of Engineering.

Student Feedback

At the end of every semester students will be invited to give feedback on the course and teaching through a tool called SET or Qualtrics. The lecturers and course co-ordinators will consider all feedback and respond with summaries and actions.

Your feedback helps teachers to improve the course and its delivery for future students.

Class Representatives in each class can take feedback to the department and faculty staff-student consultative committees.

Digital Resources

Course materials are made available in a learning and collaboration tool called Canvas which also includes reading lists and lecture recordings (where available).

Please remember that the recording of any class on a personal device requires the permission of the instructor.

Lecture notes as PDF files are available on Canvas.

Academic Integrity

The University of Auckland will not tolerate cheating, or assisting others to cheat, and views cheating in coursework as a serious academic offence. The work that a student submits for grading must be the student's own work, reflecting their learning. Where work from other sources is used, it must be properly acknowledged and referenced. This requirement also applies to sources on the internet. A student's assessed work may be reviewed against online source material using computerised detection mechanisms.

Class Representatives

Class representatives are students tasked with representing student issues to departments, faculties, and the wider university. If you have a complaint about this course, please contact your class rep who will know how to raise it in the right channels. See your departmental noticeboard for contact details for your class reps.

Inclusive Learning

All students are asked to discuss any impairment related requirements privately, face to face and/or in written form with the course coordinator, lecturer or tutor.

Student Disability Services also provides support for students with a wide range of impairments, both visible and invisible, to succeed and excel at the University. For more information and contact details, please visit the Student Disability Services’ website http://disability.auckland.ac.nz

Special Circumstances

If your ability to complete assessed coursework is affected by illness or other personal circumstances outside of your control, contact a member of teaching staff as soon as possible before the assessment is due.

If your personal circumstances significantly affect your performance, or preparation, for an exam or eligible written test, refer to the University’s aegrotat or compassionate consideration page https://www.auckland.ac.nz/en/students/academic-information/exams-and-final-results/during-exams/aegrotat-and-compassionate-consideration.html.

This should be done as soon as possible and no later than seven days after the affected test or exam date.

Learning Continuity

In the event of an unexpected disruption we undertake to maintain the continuity and standard of teaching and learning in all your courses throughout the year. If there are unexpected disruptions the University has contingency plans to ensure that access to your course continues and your assessment is fair, and not compromised. Some adjustments may need to be made in emergencies. You will be kept fully informed by your course co-ordinator, and if disruption occurs you should refer to the University Website for information about how to proceed.

Student Charter and Responsibilities

The Student Charter assumes and acknowledges that students are active participants in the learning process and that they have responsibilities to the institution and the international community of scholars. The University expects that students will act at all times in a way that demonstrates respect for the rights of other students and staff so that the learning environment is both safe and productive. For further information visit Student Charter https://www.auckland.ac.nz/en/students/forms-policies-and-guidelines/student-policies-and-guidelines/student-charter.html.


Elements of this outline may be subject to change. The latest information about the course will be available for enrolled students in Canvas.

In this course you may be asked to submit your coursework assessments digitally. The University reserves the right to conduct scheduled tests and examinations for this course online or through the use of computers or other electronic devices. Where tests or examinations are conducted online remote invigilation arrangements may be used. The final decision on the completion mode for a test or examination, and remote invigilation arrangements where applicable, will be advised to students at least 10 days prior to the scheduled date of the assessment, or in the case of an examination when the examination timetable is published.

Published on 15/07/2021 07:10 a.m.